AUV同时定位与跟踪算法研究

为了提高自治水下航行器的水下适应能力,对比目标跟踪与协同导航的概念,并仿照同时定位与制图方法,提出了基于EKF-SLAM框架和FastSLAM框架的自治水下航行器同时定位与跟踪算法.根据装备在携带低精度自定位传感器AUV上的声纳传感器持续探测非合作目标并估计目标航迹的同时,利用探测到的AUV与目标间的相对信息修正其自身航位推算带来的累积估计误差,从而提高AUV的自定位精度,AUV的定位和目标的状态估计同时进行,且要满足一定的精度.仿真比较了所提出的两种算法的精度,并验证了算法的有效性和一致性.

Algorithms research of simultaneous localization and tracking for AUVs

For enhancing the adaptive capacity of autonomous underwater vehicles（AUV）,simultaneous localization and tracking（SLAT）algorithms based on the EKF-SLAM framework and the FastSLAM framework for AUVs was proposed by comparing the concepts of target tracking and cooperative navigation,and imitating simultaneous localization and mapping（SLAM）.The simultaneous localization and tracking for an AUV in the unknown environment refers to that the sonar sensors of the AUV equipped with low precise proprioceptive localization sensors are used to detect a non-cooperative target continuously and estimate the track of the target.At the same time,the cumulative estimate errors brought by the method of the dead reckoning are corrected by utilizing the relative information between the AUV and the target,and then the localization accuracy of the AUV is improved.The localization of the AUV and state estimate of the target are carried,and they are interdependent and influencing each other and must have a certain accuracy both.At last,the accuracy of two proposed algorithms are compared by the simulation,and the validity and the consistency of the algorithm was shown.